背景:基于人工智能(AI)的工具在组织病理学图像分析中显示出有望提高口腔鳞状细胞癌(OSCC)检测的准确性,旨在减少人为错误。
目的:本系统综述和荟萃分析通过评估基于AI的医学图像分析研究的常见诊断性能评估指标,评估了组织病理学图像上OSCC检测的深度学习(DL)模型。
方法:分析了与参考标准相比,使用DL模型分析OSCC组织病理学图像的诊断准确性研究。六个数据库(PubMed,谷歌学者,Scopus,Embase,ArXiv,和IEEE)进行了无任何时间限制的出版物筛选。QUADAS-2工具用于评估质量。荟萃分析仅包括报告真阳性(TP)的研究,真负(TN),假阳性(FP),和假阴性(FN)在他们的测试集。
结果:在1267项筛选研究中,17项研究符合最终纳入标准。使用了图像分类(n=11)和分割(n=3)等DL方法,一些研究使用组合方法(n=3)。在QUADAS-2评估中,在所有适用性领域中,只有3项研究的偏倚风险较低.对于分割研究,报告了0.97的准确性,灵敏度为0.97,0.98的特异性,和0.92的骰子。对于分类研究,准确性报告为0.99,敏感性0.99,特异性1.0,Dice0.95,F1评分0.98和AUC0.99.荟萃分析显示汇总估计为0.98敏感性和0.93特异性。
结论:在图像分析中应用基于AI的分类和分割方法代表了数字病理学的根本转变。DL方法对组织病理学上的OSCC检测显示出极高的准确性,与某些研究中的人类专家相当。尽管基于AI的模型不能取代训练有素的病理学家,它们可以通过提高诊断的客观性和可重复性,同时减少病理学家倦怠导致的变异性和人为错误。
BACKGROUND: Artificial intelligence (AI)-based tools have shown promise in histopathology image analysis in improving the accuracy of oral squamous cell carcinoma (OSCC) detection with intent to reduce human error.
OBJECTIVE: This systematic review and meta-analysis evaluated deep learning (DL) models for OSCC detection on histopathology images by assessing common diagnostic performance evaluation metrics for AI-based medical image analysis studies.
METHODS: Diagnostic accuracy studies that used DL models for the analysis of histopathological images of OSCC compared to the reference standard were analyzed. Six databases (PubMed, Google Scholar, Scopus, Embase, ArXiv, and IEEE) were screened for publications without any time limitation. The QUADAS-2 tool was utilized to assess quality. The meta-analyses included only studies that reported true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) in their test sets.
RESULTS: Of 1267 screened studies, 17 studies met the final inclusion criteria. DL methods such as image classification (n = 11) and segmentation (n = 3) were used, and some studies used combined methods (n = 3). On QUADAS-2 assessment, only three studies had a low risk of bias across all applicability domains. For segmentation studies, 0.97 was reported for accuracy, 0.97 for sensitivity, 0.98 for specificity, and 0.92 for Dice. For classification studies, accuracy was reported as 0.99, sensitivity 0.99, specificity 1.0, Dice 0.95, F1 score 0.98, and AUC 0.99. Meta-analysis showed pooled estimates of 0.98 sensitivity and 0.93 specificity.
CONCLUSIONS: Application of AI-based classification and segmentation methods on image analysis represents a fundamental shift in digital pathology. DL approaches demonstrated significantly high accuracy for OSCC detection on histopathology, comparable to that of human experts in some studies. Although AI-based models cannot replace a well-trained pathologist, they can assist through improving the objectivity and repeatability of the diagnosis while reducing variability and human error as a consequence of pathologist burnout.